From c3a64bc1048481c628e542cf41cff67adc434fc0 Mon Sep 17 00:00:00 2001 From: Nemo Date: Mon, 28 Jun 2021 21:34:53 +0530 Subject: [PATCH] Increase coverage --- README.md | 45 ++ boardgame-research.rdf | 1081 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 1126 insertions(+) diff --git a/README.md b/README.md index 5a7e062..6fa5206 100644 --- a/README.md +++ b/README.md @@ -35,19 +35,25 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub]( - [Mafia](#mafia) - [Magic: The Gathering](#magic-the-gathering) - [Mobile Games](#mobile-games) +- [Modern Art](#modern-art) - [Monopoly](#monopoly) - [Monopoly Deal](#monopoly-deal) - [Nmbr9](#nmbr9) +- [Pandemic](#pandemic) - [Patchwork](#patchwork) +- [Pentago](#pentago) - [Quixo](#quixo) - [Race for the Galaxy](#race-for-the-galaxy) - [RISK](#risk) +- [Santorini](#santorini) +- [Scotland Yard](#scotland-yard) - [Secret Hitler](#secret-hitler) - [Set](#set) - [Settlers of Catan](#settlers-of-catan) - [Shobu](#shobu) - [Terra Mystica](#terra-mystica) - [Tetris Link](#tetris-link) +- [The Resistance: Avalon](#the-resistance-avalon) - [Ticket to Ride](#ticket-to-ride) - [Ultimate Tic-Tac-Toe](#ultimate-tic-tac-toe) - [UNO](#uno) @@ -147,6 +153,9 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub]( # Mafia - [A mathematical model of the Mafia game](http://arxiv.org/abs/1009.1031v3) (journalArticle) +- [Automatic Long-Term Deception Detection in Group Interaction Videos](http://arxiv.org/abs/1905.08617) (journalArticle) +- [Human-Side Strategies in the Werewolf Game Against the Stealth Werewolf Strategy](http://link.springer.com/10.1007/978-3-319-50935-8_9) (bookSection) +- [A Theoretical Study of Mafia Games](http://arxiv.org/abs/0804.0071) (journalArticle) # Magic: The Gathering - [Ensemble Determinization in Monte Carlo Tree Search for the Imperfect Information Card Game Magic: The Gathering](https://doi.org/10.1109%2Ftciaig.2012.2204883) (journalArticle) @@ -155,13 +164,33 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub]( - [Magic: the Gathering is as Hard as Arithmetic](http://arxiv.org/abs/2003.05119v1) (journalArticle) - [Magic: The Gathering is Turing Complete](http://arxiv.org/abs/1904.09828v2) (journalArticle) - [Neural Networks Models for Analyzing Magic: the Gathering Cards](http://arxiv.org/abs/1810.03744v1) (journalArticle) +- [Neural Networks Models for Analyzing Magic: The Gathering Cards](http://link.springer.com/10.1007/978-3-030-04179-3_20) (bookSection) +- [The Complexity of Deciding Legality of a Single Step of Magic: The Gathering](https://livrepository.liverpool.ac.uk/3029568/) (conferencePaper) +- [Magic: The Gathering in Common Lisp](https://vixra.org/abs/2001.0065) (conferencePaper) +- [Magic: The Gathering in Common Lisp](https://github.com/jeffythedragonslayer/maglisp) (computerProgram) +- [Mathematical programming and Magic: The Gathering](https://commons.lib.niu.edu/handle/10843/19194) (thesis) +- [Deck Construction Strategies for Magic: The Gathering](https://www.doi.org/10.1685/CSC06077) (conferencePaper) +- [Deckbuilding in Magic: The Gathering Using a Genetic Algorithm](https://doi.org/11250/2462429) (thesis) +- [Magic: The Gathering Deck Performance Prediction](http://cs229.stanford.edu/proj2012/HauPlotkinTran-MagicTheGatheringDeckPerformancePrediction.pdf) (report) # Mobile Games - [Trainyard is NP-Hard](http://arxiv.org/abs/1603.00928v1) (journalArticle) - [Threes!, Fives, 1024!, and 2048 are Hard](http://arxiv.org/abs/1505.04274v1) (journalArticle) +# Modern Art +- [A constraint programming based solver for Modern Art](https://github.com/captn3m0/modernart) (computerProgram) + # Monopoly - [Monopoly as a Markov Process](https://doi.org/10.1080%2F0025570x.1972.11976187) (journalArticle) +- [Learning Monopoly Gameplay: A Hybrid Model-Free Deep Reinforcement Learning and Imitation Learning Approach](http://arxiv.org/abs/2103.00683) (journalArticle) +- [Negotiation strategy of agents in the MONOPOLY game](http://ieeexplore.ieee.org/document/1013210/) (conferencePaper) +- [Generating interesting Monopoly boards from open data](http://ieeexplore.ieee.org/document/6374168/) (conferencePaper) +- [Estimating the probability that the game of Monopoly never ends](http://ieeexplore.ieee.org/document/5429349/) (conferencePaper) +- [Learning to Play Monopoly with Monte Carlo Tree Search](https://project-archive.inf.ed.ac.uk/ug4/20181042/ug4_proj.pdf) (report) +- [Monopoly Using Reinforcement Learning](https://ieeexplore.ieee.org/document/8929523/) (conferencePaper) +- [A Markovian Exploration of Monopoly](https://pi4.math.illinois.edu/wp-content/uploads/2014/10/Gartland-Burson-Ferguson-Markovopoly.pdf) (report) +- [Learning to play Monopoly: A Reinforcement Learning approach](https://intelligence.csd.auth.gr/publication/conference-papers/learning-to-play-monopoly-a-reinforcement-learning-approach/) (conferencePaper) +- [What’s the Best Monopoly Strategy?](https://core.ac.uk/download/pdf/48614184.pdf) (presentation) # Monopoly Deal - [Implementation of Artificial Intelligence with 3 Different Characters of AI Player on “Monopoly Deal” Computer Game](https://doi.org/10.1007%2F978-3-662-46742-8_11) (bookSection) @@ -170,11 +199,17 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub]( - [Nmbr9 as a Constraint Programming Challenge](http://arxiv.org/abs/2001.04238) (journalArticle) - [Nmbr9 as a Constraint Programming Challenge](https://zayenz.se/blog/post/nmbr9-cp2019-abstract/) (blogPost) +# Pandemic +- [NP-Completeness of Pandemic](https://www.jstage.jst.go.jp/article/ipsjjip/20/3/20_723/_article) (journalArticle) + # Patchwork - [State Representation and Polyomino Placement for the Game Patchwork](https://zayenz.se/blog/post/patchwork-modref2019-paper/) (blogPost) - [State Representation and Polyomino Placement for the Game Patchwork](http://arxiv.org/abs/2001.04233) (journalArticle) - [State Representation and Polyomino Placement for the Game Patchwork](https://zayenz.se/papers/Lagerkvist_ModRef_2019_Presentation.pdf) (presentation) +# Pentago +- [On Solving Pentago](http://www.ke.tu-darmstadt.de/lehre/arbeiten/bachelor/2011/Buescher_Niklas.pdf) (thesis) + # Quixo - [QUIXO is EXPTIME-complete](https://doi.org/10.1016%2Fj.ipl.2020.105995) (journalArticle) - [Quixo Is Solved](http://arxiv.org/abs/2007.15895) (journalArticle) @@ -193,6 +228,13 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub]( - [RISK Board Game ‐ Battle Outcome Analysis](http://www.c4i.gr/xgeorgio/docs/RISK-board-game%20_rev-3.pdf) (journalArticle) - [A multi-agent system for playing the board game risk]() (book) +# Santorini +- [A Mathematical Analysis of the Game of Santorini](https://openworks.wooster.edu/independentstudy/8917/) (thesis) +- [A Mathematical Analysis of the Game of Santorini](https://github.com/carsongeissler/SantoriniIS) (computerProgram) + +# Scotland Yard +- [The complexity of Scotland Yard](https://eprints.illc.uva.nl/id/eprint/193/1/PP-2006-18.text.pdf) (report) + # Secret Hitler - [Competing in a Complex Hidden Role Game with Information Set Monte Carlo Tree Search](http://arxiv.org/abs/2005.07156) (journalArticle) @@ -226,6 +268,9 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub]( # Tetris Link - [A New Challenge: Approaching Tetris Link with AI](http://arxiv.org/abs/2004.00377) (journalArticle) +# The Resistance: Avalon +- [Finding Friend and Foe in Multi-Agent Games](http://arxiv.org/abs/1906.02330) (journalArticle) + # Ticket to Ride - [AI-based playtesting of contemporary board games](http://dl.acm.org/citation.cfm?doid=3102071.3102105) (conferencePaper) - [Materials for Ticket to Ride Seattle and a framework for making more game boards](https://github.com/dovinmu/ttr_generator) (computerProgram) diff --git a/boardgame-research.rdf b/boardgame-research.rdf index 2ceca64..6143c4c 100644 --- a/boardgame-research.rdf +++ b/boardgame-research.rdf @@ -5770,6 +5770,1042 @@ DOI: 10.1007/978-3-319-71649-7_5 1 text/html + + journalArticle + + arXiv:1905.08617 [cs] + + + + + + Bai + Chongyang + + + + + Bolonkin + Maksim + + + + + Burgoon + Judee + + + + + Chen + Chao + + + + + Dunbar + Norah + + + + + Singh + Bharat + + + + + Subrahmanian + V. S. + + + + + Wu + Zhe + + + + + + + + + Computer Science - Artificial Intelligence + + + + + Computer Science - Computer Vision and Pattern Recognition + + + Automatic Long-Term Deception Detection in Group Interaction Videos + Most work on automated deception detection (ADD) in video has two restrictions: (i) it focuses on a video of one person, and (ii) it focuses on a single act of deception in a one or two minute video. In this paper, we propose a new ADD framework which captures long term deception in a group setting. We study deception in the well-known Resistance game (like Mafia and Werewolf) which consists of 5-8 players of whom 2-3 are spies. Spies are deceptive throughout the game (typically 30-65 minutes) to keep their identity hidden. We develop an ensemble predictive model to identify spies in Resistance videos. We show that features from low-level and high-level video analysis are insufficient, but when combined with a new class of features that we call LiarRank, produce the best results. We achieve AUCs of over 0.70 in a fully automated setting. Our demo can be found at http://home.cs.dartmouth.edu/~mbolonkin/scan/demo/ + 2019-06-15 + arXiv.org + + + http://arxiv.org/abs/1905.08617 + + + 2021-06-28 15:32:49 + arXiv: 1905.08617 + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/1905.08617.pdf + + + 2021-06-28 15:32:54 + 1 + application/pdf + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/1905.08617 + + + 2021-06-28 15:32:58 + 1 + text/html + + + bookSection + + + 10068 + ISBN 978-3-319-50934-1 978-3-319-50935-8 + Computers and Games + + + + + + + Cham + + + Springer International Publishing + + + + + + + Plaat + Aske + + + + + Kosters + Walter + + + + + van den Herik + Jaap + + + + + + + + + Bi + Xiaoheng + + + + + Tanaka + Tetsuro + + + + + + Human-Side Strategies in the Werewolf Game Against the Stealth Werewolf Strategy + 2016 + DOI.org (Crossref) + + + http://link.springer.com/10.1007/978-3-319-50935-8_9 + + + 2021-06-28 15:32:54 + Series Title: Lecture Notes in Computer Science +DOI: 10.1007/978-3-319-50935-8_9 + 93-102 + + + attachment + Full Text + + + https://sci-hub.se/downloads/2019-01-26//f7/bi2016.pdf#view=FitH + + + 2021-06-28 15:33:08 + 1 + application/pdf + + + journalArticle + + arXiv:0804.0071 [math] + + + + + + Yao + Erlin + + + + + + + + 65C20 + + + 91-01 + + + + Mathematics - Probability + + + A Theoretical Study of Mafia Games + Mafia can be described as an experiment in human psychology and mass hysteria, or as a game between informed minority and uninformed majority. Focus on a very restricted setting, Mossel et al. [to appear in Ann. Appl. Probab. Volume 18, Number 2] showed that in the mafia game without detectives, if the civilians and mafias both adopt the optimal randomized strategy, then the two groups have comparable probabilities of winning exactly when the total player size is R and the mafia size is of order Sqrt(R). They also proposed a conjecture which stated that this phenomenon should be valid in a more extensive framework. In this paper, we first indicate that the main theorem given by Mossel et al. [to appear in Ann. Appl. Probab. Volume 18, Number 2] can not guarantee their conclusion, i.e., the two groups have comparable winning probabilities when the mafia size is of order Sqrt(R). Then we give a theorem which validates the correctness of their conclusion. In the last, by proving the conjecture proposed by Mossel et al. [to appear in Ann. Appl. Probab. Volume 18, Number 2], we generalize the phenomenon to a more extensive framework, of which the mafia game without detectives is only a special case. + 2008-04-01 + arXiv.org + + + http://arxiv.org/abs/0804.0071 + + + 2021-06-28 15:33:04 + arXiv: 0804.0071 + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/0804.0071.pdf + + + 2021-06-28 15:33:07 + 1 + application/pdf + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/0804.0071 + + + 2021-06-28 15:33:10 + 1 + text/html + + + bookSection + + + 11302 + ISBN 978-3-030-04178-6 978-3-030-04179-3 + Neural Information Processing + + + + + + + Cham + + + Springer International Publishing + + + + + + + Cheng + Long + + + + + Leung + Andrew Chi Sing + + + + + Ozawa + Seiichi + + + + + + + + + Zilio + Felipe + + + + + Prates + Marcelo + + + + + Lamb + Luis + + + + + + Neural Networks Models for Analyzing Magic: The Gathering Cards + 2018 + Neural Networks Models for Analyzing Magic + DOI.org (Crossref) + + + http://link.springer.com/10.1007/978-3-030-04179-3_20 + + + 2021-06-28 15:33:26 + Series Title: Lecture Notes in Computer Science +DOI: 10.1007/978-3-030-04179-3_20 + 227-239 + + + attachment + Submitted Version + + + https://arxiv.org/pdf/1810.03744 + + + 2021-06-28 15:33:36 + 1 + application/pdf + + + conferencePaper + + + + The Complexity of Deciding Legality of a Single Step of Magic: The Gathering + + + https://livrepository.liverpool.ac.uk/3029568/ + + + + + conferencePaper + + + + Magic: The Gathering in Common Lisp + + + https://vixra.org/abs/2001.0065 + + + + + computerProgram + Magic: The Gathering in Common Lisp + + + https://github.com/jeffythedragonslayer/maglisp + + + + + thesis + Mathematical programming and Magic: The Gathering + + + https://commons.lib.niu.edu/handle/10843/19194 + + + + + conferencePaper + + + + Deck Construction Strategies for Magic: The Gathering + + + https://www.doi.org/10.1685/CSC06077 + + + + + thesis + Deckbuilding in Magic: The Gathering Using a Genetic Algorithm + + + https://doi.org/11250/2462429 + + + + + report + Magic: The Gathering Deck Performance Prediction + + + http://cs229.stanford.edu/proj2012/HauPlotkinTran-MagicTheGatheringDeckPerformancePrediction.pdf + + + + + computerProgram + A constraint programming based solver for Modern Art + + + https://github.com/captn3m0/modernart + + + + + journalArticle + + arXiv:2103.00683 [cs] + + + + + + Haliem + Marina + + + + + Bonjour + Trevor + + + + + Alsalem + Aala + + + + + Thomas + Shilpa + + + + + Li + Hongyu + + + + + Aggarwal + Vaneet + + + + + Bhargava + Bharat + + + + + Kejriwal + Mayank + + + + + + + + + Computer Science - Artificial Intelligence + + + + + Computer Science - Machine Learning + + + Learning Monopoly Gameplay: A Hybrid Model-Free Deep Reinforcement Learning and Imitation Learning Approach + Learning how to adapt and make real-time informed decisions in dynamic and complex environments is a challenging problem. To learn this task, Reinforcement Learning (RL) relies on an agent interacting with an environment and learning through trial and error to maximize the cumulative sum of rewards received by it. In multi-player Monopoly game, players have to make several decisions every turn which involves complex actions, such as making trades. This makes the decision-making harder and thus, introduces a highly complicated task for an RL agent to play and learn its winning strategies. In this paper, we introduce a Hybrid Model-Free Deep RL (DRL) approach that is capable of playing and learning winning strategies of the popular board game, Monopoly. To achieve this, our DRL agent (1) starts its learning process by imitating a rule-based agent (that resembles the human logic) to initialize its policy, (2) learns the successful actions, and improves its policy using DRL. Experimental results demonstrate an intelligent behavior of our proposed agent as it shows high win rates against different types of agent-players. + 2021-02-28 + Learning Monopoly Gameplay + arXiv.org + + + http://arxiv.org/abs/2103.00683 + + + 2021-06-28 15:48:08 + arXiv: 2103.00683 + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/2103.00683.pdf + + + 2021-06-28 15:48:19 + 1 + application/pdf + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/2103.00683 + + + 2021-06-28 15:48:23 + 1 + text/html + + + conferencePaper + + + ISBN 978-0-7803-7203-0 + Proceedings 2001 IEEE International Symposium on Computational Intelligence in Robotics and Automation (Cat. No.01EX515) + DOI 10.1109/CIRA.2001.1013210 + + + + + + + Banff, Alta., Canada + + + IEEE + + + + + + + Yasumura + Y. + + + + + Oguchi + K. + + + + + Nitta + K. + + + + + + Negotiation strategy of agents in the MONOPOLY game + 2001 + DOI.org (Crossref) + + + http://ieeexplore.ieee.org/document/1013210/ + + + 2021-06-28 15:49:10 + 277-281 + + + 2001 International Symposium on Computational Intelligence in Robotics and Automation + + + + + attachment + Full Text + + + https://moscow.sci-hub.se/3317/19346a5b777c1582800b51ee3a7cf5ed/negotiation-strategy-of-agents-in-the-monopoly-game.pdf#view=FitH + + + 2021-06-28 15:49:15 + 1 + application/pdf + + + conferencePaper + + + ISBN 978-1-4673-1194-6 978-1-4673-1193-9 978-1-4673-1192-2 + 2012 IEEE Conference on Computational Intelligence and Games (CIG) + DOI 10.1109/CIG.2012.6374168 + + + + + + + Granada, Spain + + + IEEE + + + + + + + Friberger + Marie Gustafsson + + + + + Togelius + Julian + + + + + + Generating interesting Monopoly boards from open data + 09/2012 + DOI.org (Crossref) + + + http://ieeexplore.ieee.org/document/6374168/ + + + 2021-06-28 15:49:18 + 288-295 + + + 2012 IEEE Conference on Computational Intelligence and Games (CIG) + + + + + attachment + Submitted Version + + + http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=81CA58D9ACCE8CA7412077093E520EFC?doi=10.1.1.348.6099&rep=rep1&type=pdf + + + 2021-06-28 15:49:32 + 1 + application/pdf + + + conferencePaper + + + ISBN 978-1-4244-5770-0 978-1-4244-5771-7 + Proceedings of the 2009 Winter Simulation Conference (WSC) + DOI 10.1109/WSC.2009.5429349 + + + + + + + Austin, TX, USA + + + IEEE + + + + + + + Friedman + Eric J. + + + + + Henderson + Shane G. + + + + + Byuen + Thomas + + + + + Gallardo + German Gutierrez + + + + + + Estimating the probability that the game of Monopoly never ends + 12/2009 + DOI.org (Crossref) + + + http://ieeexplore.ieee.org/document/5429349/ + + + 2021-06-28 15:49:23 + 380-391 + + + 2009 Winter Simulation Conference (WSC 2009) + + + + + attachment + Full Text + + + https://moscow.sci-hub.se/3233/bacac19e84c764b72c627d05f55c0ad9/friedman2009.pdf#view=FitH + + + 2021-06-28 15:49:32 + 1 + application/pdf + + + report + Learning to Play Monopoly with Monte Carlo Tree Search + + + https://project-archive.inf.ed.ac.uk/ug4/20181042/ug4_proj.pdf + + + + + conferencePaper + + + ISBN 978-1-72811-895-6 + TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON) + DOI 10.1109/TENCON.2019.8929523 + + + + + + + Kochi, India + + + IEEE + + + + + + + Arun + Edupuganti + + + + + Rajesh + Harikrishna + + + + + Chakrabarti + Debarka + + + + + Cherala + Harikiran + + + + + George + Koshy + + + + + + Monopoly Using Reinforcement Learning + 10/2019 + DOI.org (Crossref) + + + https://ieeexplore.ieee.org/document/8929523/ + + + 2021-06-28 15:49:50 + 858-862 + + + TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON) + + + + + attachment + Full Text + + + https://sci-hub.se/downloads/2020-04-10/35/arun2019.pdf?rand=60d9ef9f20b26#view=FitH + + + 2021-06-28 15:50:07 + 1 + application/pdf + + + report + A Markovian Exploration of Monopoly + + + https://pi4.math.illinois.edu/wp-content/uploads/2014/10/Gartland-Burson-Ferguson-Markovopoly.pdf + + + + + conferencePaper + + + + Learning to play Monopoly: A Reinforcement Learning approach + + + https://intelligence.csd.auth.gr/publication/conference-papers/learning-to-play-monopoly-a-reinforcement-learning-approach/ + + + + + presentation + What’s the Best Monopoly Strategy? + + + https://core.ac.uk/download/pdf/48614184.pdf + + + + + journalArticle + + + + + + Nakai + Kenichiro + + + + + Takenaga + Yasuhiko + + + + + + NP-Completeness of Pandemic + 2012 + en + DOI.org (Crossref) + + + https://www.jstage.jst.go.jp/article/ipsjjip/20/3/20_723/_article + + + 2021-06-28 15:59:47 + 723-726 + + + 20 + Journal of Information Processing + DOI 10.2197/ipsjjip.20.723 + 3 + Journal of Information Processing + ISSN 1882-6652 + + + attachment + Full Text + + + https://www.jstage.jst.go.jp/article/ipsjjip/20/3/20_723/_pdf + + + 2021-06-28 15:59:50 + 1 + application/pdf + + + thesis + On Solving Pentago + + + http://www.ke.tu-darmstadt.de/lehre/arbeiten/bachelor/2011/Buescher_Niklas.pdf + + + + + journalArticle + + + arXiv:1906.02330 [cs, stat] + + + + + + + Serrino + Jack + + + + + Kleiman-Weiner + Max + + + + + Parkes + David C. + + + + + Tenenbaum + Joshua B. + + + + + + + + + Computer Science - Machine Learning + + + + + Computer Science - Multiagent Systems + + + + + Statistics - Machine Learning + + + Finding Friend and Foe in Multi-Agent Games + Recent breakthroughs in AI for multi-agent games like Go, Poker, and Dota, have seen great strides in recent years. Yet none of these games address the real-life challenge of cooperation in the presence of unknown and uncertain teammates. This challenge is a key game mechanism in hidden role games. Here we develop the DeepRole algorithm, a multi-agent reinforcement learning agent that we test on The Resistance: Avalon, the most popular hidden role game. DeepRole combines counterfactual regret minimization (CFR) with deep value networks trained through self-play. Our algorithm integrates deductive reasoning into vector-form CFR to reason about joint beliefs and deduce partially observable actions. We augment deep value networks with constraints that yield interpretable representations of win probabilities. These innovations enable DeepRole to scale to the full Avalon game. Empirical game-theoretic methods show that DeepRole outperforms other hand-crafted and learned agents in five-player Avalon. DeepRole played with and against human players on the web in hybrid human-agent teams. We find that DeepRole outperforms human players as both a cooperator and a competitor. + 2019-06-05 + arXiv.org + + + http://arxiv.org/abs/1906.02330 + + + 2021-06-28 16:00:28 + arXiv: 1906.02330 + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/1906.02330.pdf + + + 2021-06-28 16:00:35 + 1 + application/pdf + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/1906.02330 + + + 2021-06-28 16:00:38 + 1 + text/html + + + thesis + A Mathematical Analysis of the Game of Santorini + + + https://openworks.wooster.edu/independentstudy/8917/ + + + + + computerProgram + A Mathematical Analysis of the Game of Santorini + + + https://github.com/carsongeissler/SantoriniIS + + + + + report + The complexity of Scotland Yard + + + https://eprints.illc.uva.nl/id/eprint/193/1/PP-2006-18.text.pdf + + + 2048 @@ -5876,6 +6912,9 @@ DOI: 10.1007/978-3-319-71649-7_5 Mafia + + + Magic: The Gathering @@ -5885,15 +6924,36 @@ DOI: 10.1007/978-3-319-71649-7_5 + + + + + + + + Mobile Games + + Modern Art + + Monopoly + + + + + + + + + Monopoly Deal @@ -5904,12 +6964,20 @@ DOI: 10.1007/978-3-319-71649-7_5 + + Pandemic + + Patchwork + + Pentago + + Quixo @@ -5931,6 +6999,15 @@ DOI: 10.1007/978-3-319-71649-7_5 + + Santorini + + + + + Scotland Yard + + Secret Hitler @@ -5970,6 +7047,10 @@ DOI: 10.1007/978-3-319-71649-7_5 Tetris Link + + The Resistance: Avalon + + Ticket to Ride